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Precipitation and Cloud Structure in Midlatitude Cyclones

This study examines the structure of clouds, precipitation, and winds in midlatitude cyclones using a multisensor database. The aim is to evaluate the performance of climate models and understand the implications of cyclones for climate change.

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Precipitation and Cloud Structure in Midlatitude Cyclones

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  1. Precipitation and Cloud Structure in Midlatitude Cyclones Rob Wood with Paul Field, NCAR

  2. stable unstable Why cyclones? Solberg (1928) examined all types of waves in the atmosphere using a two-level baroclinic model: Short waves stabilized by gravity Long waves stabilized inertially (earth’s rotation) lengthscale from Bjerknes and Holmboe, J. Met. (1944)

  3. When, where? Zonal wind pressure [hPa] • Zonal winds peak in hemisphere with strongest T/y  large growth rates for baroclinic disturbances DJF Temperature 90S 60S 30S EQ 30N 60N 90N Holton, 1992

  4. Hemispherical view 500 hPa geopotential height wavenumber 7   4000 km N May 17 2007

  5. The structure of a midlatitude cyclone From Schultz et al. (1998) C O L D W A R M

  6. Idealized cyclone Carlson 1998 L

  7. Multisensor cyclone database • Synthesize data from useful satellite datasets and reanalysis to examine mid-latitude cyclone structure • Cyclone-relative coordinate system to facilitate comparisons and composite analysis • Make available to community • www.atmos.washington.edu/~robwood/data/cyclones.html

  8. Cloud Compositing H L Lau and Crane 1995

  9. Satellite data • MODIS: Cloud top temperature and optical thickness (11o daily data) • AMSR: Rain rate; column WVP; SST • Quikscat: Surface wind speed and direction • All available data from 2003-2004 • ~2000 cyclones in total for NP, NA, SP, SA over oceanic regions • NCEP: reanalysis MSLP, T profiles (WVPsat)

  10. Regions examined Major midlatitude storm track regions of the northern and southern hemispheres

  11. Identifying and compositing cyclones

  12. Cyclone strength and size: Region

  13. Cyclone strength and size: Season Seasons reversed for SH storms Mak 1982

  14. Quikscat winds (all NA storms) [m s-1]

  15. AMSR water vapor path (all NA storms) [mm] Warm sector km

  16. Warm Conveyor Belt thermodynamic dynamic R = WVPV W V S q0 = WVP / S

  17. Generate new composites (combine NA, NP, SA, SP) Strength Moisture V r<2000 km WVP r<2000 km Quikscat windspeed WVP

  18. Warm conveyor model R = WVP V • Simple model quantifies cyclone-wide precip. as a function of its strength and WVP • WVP controlled by SST Clausius -Clapeyron

  19. Column mean relative humidity RHcol = WVP/ WVPsat invariant to changes in storm strength or vapor

  20. Cloud Top Temperature All N Atl. cyclones

  21. Cloud Top Temperature Normalised pdfs of composited cyclones Rainrate km km All N Atl. cyclones km Bjerknes and Solberg

  22. p<440 hPa

  23. Implications: precipitation and storm density under a warmed climate • dRext + dRT = LdP • LdP = k dT - 2.5 • k  3 W m-2 K-1 (quite robust across models) number and/or strength of storms must decrease Allen and Ingram, Nature, 2002 also Mitchell et al. 1987

  24. Implications: cloud feedback • Fewer and/or weaker storms under warmed climate  less high cloud  more OLR  negative feedback • But, assumes constant albedo (“midlatitude blanket” argument) • Future work: Test using radiative transfer calculations/CERES

  25. Conclusions • Multisensor database of midlatitude cyclones • Preliminary exploration of the structure of clouds, precip, and winds in midlatitude systems • Expand to longer timescales/space scales to improve statistics • Use to evaluate the performance of climate models

  26. Cyclones and climate change • Carnell and Senior (change in distribution of storm strength) • Poleward movement of the storm tracks (Fyfe, Yin, Bengtsson, Fu et al., Lorenz, Salathe…) • More precipitation/less precipitation?

  27. 6 4 2 0 -2 -4 -6 -8 change in number of storms 1025 1015 1005 995 985 975 965 955 945 storm central pressure [hPa] Fewer weak storms, more strong ones? Carnell and Senior, Clim. Dyn., 1998

  28. 18 15 12 9 6 3 0 Storm tracks Number of storms per month per 5x5o area Bengtsson et al. (2006)

  29. Poleward storm track movement? Change in number of storms per month per 5x5o area (C 21st- C 20th) DJF JJA Also, Fu et al. (MSU), Yin, Fyfe…. 3 2 1 0 -1 -2 -3 Bengtsson et al. (2006) Mean values 5-15

  30. Conclusions

  31. THE END

  32. cloudparameterizations               Uncertainty about behavior of clouds is the leading cause of spread of temperatures under 2CO2 conditions Murphy et al. 2004 Nature Stainforth et al. 2005 Nature

  33. Levels of understanding • Bjerknes – high clouds and precipitation associated with ascending regions of the cyclone • QG theory – spatiotemporal variations and strength of ascending regions understood as first order response to…. • Marriage of QG theory and moisture (latent heating) incomplete • Limitations upon what quantitative questions can be answered from a theoretical perspective (e.g. climatological response of cyclones to changing baroclinicity

  34. RHwater Column (NA) km

  35. But no consensus….. ECHAM-5 Bengtsson et al., J. Clim., 2006 Trends in cyclone strength dependent upon diagnostic used to define storm strength (pressure depth vs vorticity) Number per month No increase in strong storms 0 5 10 15 20 Storm intensity [10-5 s-1]

  36. Compositing cyclone center

  37. Cyclones in the community atmosphere model (CAM) • Community Atmosphere Model (CAM 3.0) run for 5 years with different model configurations/parameterizations • Identical cyclone location methodology • Different model configurations: control : standard version but at 1x1o resolution micro : with new microphysics scheme (Morrison) ssat : increase critical RH for ice (from 90% to 100%) nodeep : deep convection scheme turned off noconv : deep and shallow convection schemes turned off 4x5 : lower resolution (4x5o) Thanks to Rich Neal, Andrew Gettelman, Phil Rasch

  38. Cyclone mean rain rate in CAM • Models show similar increases in R with WVP consistent with obs. • Model R is more strength-dependent than obs. models obs

  39. Clausius-Clapeyron constraints • Clausius-Clapeyron constrains water vapor changes with SST in model and obs. cyclones Lapse rate changes needed to explain WVP changes z T

  40. Clausius-Clapeyron constraints • Clausius-Clapeyron constrains precipitation changes in model and obs. cyclones  Large-scale precipitation changes governed by changes in cyclone strength and frequency

  41. High clouds • Very sensitive to model configuration • Models show stronger moisture dependence than observations

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